Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

Boeing, Geoff; Besbris, Max; Schachter, Ariela; Kuk, John

doi:10.31235/osf.io/n5zws

Cited by 4 publications

(4 citation statements)

References 7 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, advertisements for rental housing on Craigslist must contain advertised rent and a textual description of the rental unit ( Besbris et al, in press ; Kennedy et al, 2020 ). The vast majority also contain an exact address or easily geocoded location on a map embedded in the ad, pictures of the unit, and specific information about the pet policy, access to laundry services, the square footage, and the number of bedrooms and bathrooms ( Boeing et al, 2021 ). Despite their advantages, propriety data sets are not perfect representations of the rental market writ large ( Boeing, 2020 ).…”

Section: The Rental Marketmentioning

confidence: 99%

The COVID-19 Pandemic and the Rental Market: Evidence From Craigslist

Kuk

Schachter

Faber

et al. 2021

American Behavioral Scientist

Self Cite

View full text Add to dashboard Cite

Past research has demonstrated the racially and spatially uneven impacts of economic shocks and environmental disasters on various markets. In this article, we examine if and how the first few months of the COVID-19 pandemic affected the market for rental housing in the 49 largest metropolitan areas in the United States. Using a unique data set of new rental listings gathered from Craigslist and localized measures of the pandemic’s severity we find that, from mid-March to early June, local spread of COVID-19 is followed by reduced median and mean rent. However, this trend is driven by dropping rents for listings in Black, Latino, and diverse neighborhoods. Listings in majority White neighborhoods experience rent increases during this time. Our analyses make multiple contributions. First, we add to the burgeoning literature examining the rental market as a key site of perpetuating sociospatial inequality. Second, we demonstrate the utility of data gathered online for analyzing housing. And third, by reflecting on research that shows how past crises have increased sociospatial inequality and up-to-date work showing the racially and spatially unequal effects of the COVID-19 pandemic, we discuss some possible mechanisms by which the pandemic may be affecting the market for rental housing as well as implications for long-term trends.

show abstract

Section: The Rental Marketmentioning

confidence: 99%

The COVID-19 Pandemic and the Rental Market: Evidence From Craigslist

Kuk

Schachter

Faber

et al. 2021

American Behavioral Scientist

Self Cite

View full text Add to dashboard Cite

show abstract

“…Beyond the volume of information, variation also exists in the type and quality of information. Craigslist rental listings in whiter or less-poor census tracts tend to include more words (i.e., information) than listings in less-white or poorer tracts (Boeing et al 2020a ). They also provide more information about available amenities, such as the presence of a washer/dryer or whether pets are allowed, that homeseekers routinely use to filter their searches.…”

Section: Reproduction Of Systematic Biasesmentioning

confidence: 99%

Tilted platforms: rental housing technology and the rise of urban big data oligopolies

et al. 2021

Self Cite

View full text Add to dashboard Cite

This article interprets emerging scholarship on rental housing platforms—particularly the most well-known and used short- and long-term rental housing platforms—and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators’ own financial and political interests. The question of which data—and people—are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations. Policy and practice recommendations As rental housing technologies upend traditional market processes in favor of platform oligopolies, policymakers must reorient these processes toward the public good. Long-term and short-term rental platforms offer different market benefits and drawbacks, but the latter in particular requires proactive regulation to mitigate harm. At a minimum, policymakers must require that short-term rental platforms provide the information necessary for cities to enforce current, let alone new, housing regulations. Practitioners should be cautious inferring market conditions from rental housing platform data, due to difficult-to-measure sampling biases.

show abstract

“…Zoopla marks an example of a platform business model: the website o ers its services to house buyers for free, but the users pay in the form of the data they provide. People who want to sell a property have an incentive to provide accurate information about the building or apartment (Boeing, 2020;Boeing et al, 2020). Users signal their preferences in searching in speci c neighbourhoods or applying lter options to get results that are relevant to them.…”

Section: Data-driven Marketsmentioning

confidence: 99%